






"""simple docstring"""





import json
import os
import shutil
import tempfile
import unittest

import numpy as np

from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import VisionTextDualEncoderProcessor, ViTImageProcessor






@require_tokenizers
@require_vision
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				str   )	->						Optional[Any]:
      lowerCamelCase_       	=		tempfile.mkdtemp()

      # fmt: off
      lowerCamelCase_       	=		['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing', ',', 'low', 'lowest']
      # fmt: on
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as vocab_writer:
        vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]   )   )

      lowerCamelCase_       	=		{
          'do_resize': True,
          'size': {'height': 18, 'width': 18},
          'do_normalize': True,
          'image_mean': [0.5, 0.5, 0.5],
          'image_std': [0.5, 0.5, 0.5],
      }
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		__SCREAMING_SNAKE_CASE   )
      with open(self.image_processor_file			,		'w'			,		encoding='utf-8'   ) as fp:
        json.dump(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Optional[Any]			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						Tuple:
      return BertTokenizer.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]			,		**__SCREAMING_SNAKE_CASE	:				int   )	->						str:
      return ViTImageProcessor.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict   )	->						Tuple:
      shutil.rmtree(self.tmpdirname   )






    def   UpperCamelCase			(			self	:				str   )	->						List[Any]:
      lowerCamelCase_       	=		[np.random.randint(255			,		size=(3, 30, 400)			,		dtype=np.uinta   )]

      lowerCamelCase_       	=		[Image.fromarray(np.moveaxis(__SCREAMING_SNAKE_CASE			,		0			,		-1   )   ) for x in image_inputs]

      return image_inputs






    def   UpperCamelCase			(			self	:				Tuple   )	->						List[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		self.get_image_processor()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      processor.save_pretrained(self.tmpdirname   )
      lowerCamelCase_       	=		VisionTextDualEncoderProcessor.from_pretrained(self.tmpdirname   )

      self.assertEqual(processor.tokenizer.get_vocab()			,		tokenizer.get_vocab()   )
      self.assertIsInstance(processor.tokenizer			,		(BertTokenizer, BertTokenizerFast)   )

      self.assertEqual(processor.image_processor.to_json_string()			,		image_processor.to_json_string()   )
      self.assertIsInstance(processor.image_processor			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str   )	->						Optional[int]:
      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(
          tokenizer=self.get_tokenizer()			,		image_processor=self.get_image_processor()   )
      processor.save_pretrained(self.tmpdirname   )

      lowerCamelCase_       	=		self.get_tokenizer(bos_token='(BOS)'			,		eos_token='(EOS)'   )
      lowerCamelCase_       	=		self.get_image_processor(do_normalize=__SCREAMING_SNAKE_CASE			,		padding_value=1.0   )

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor.from_pretrained(
          self.tmpdirname			,		bos_token='(BOS)'			,		eos_token='(EOS)'			,		do_normalize=__SCREAMING_SNAKE_CASE			,		padding_value=1.0   )

      self.assertEqual(processor.tokenizer.get_vocab()			,		tokenizer_add_kwargs.get_vocab()   )
      self.assertIsInstance(processor.tokenizer			,		(BertTokenizer, BertTokenizerFast)   )

      self.assertEqual(processor.image_processor.to_json_string()			,		image_processor_add_kwargs.to_json_string()   )
      self.assertIsInstance(processor.image_processor			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Tuple   )	->						Any:
      lowerCamelCase_       	=		self.get_image_processor()
      lowerCamelCase_       	=		self.get_tokenizer()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		self.prepare_image_inputs()

      lowerCamelCase_       	=		image_processor(__SCREAMING_SNAKE_CASE			,		return_tensors='np'   )
      lowerCamelCase_       	=		processor(images=__SCREAMING_SNAKE_CASE			,		return_tensors='np'   )

      for key in input_feat_extract.keys():
        self.assertAlmostEqual(input_feat_extract[key].sum()			,		input_processor[key].sum()			,		delta=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Dict:
      lowerCamelCase_       	=		self.get_image_processor()
      lowerCamelCase_       	=		self.get_tokenizer()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		'lower newer'

      lowerCamelCase_       	=		processor(text=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE   )

      for key in encoded_tok.keys():
        self.assertListEqual(encoded_tok[key]			,		encoded_processor[key]   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_image_processor()
      lowerCamelCase_       	=		self.get_tokenizer()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		self.prepare_image_inputs()

      lowerCamelCase_       	=		processor(text=__SCREAMING_SNAKE_CASE			,		images=__SCREAMING_SNAKE_CASE   )

      self.assertListEqual(list(inputs.keys()   )			,		['input_ids', 'token_type_ids', 'attention_mask', 'pixel_values']   )

      # test if it raises when no input is passed
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        processor()






    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Dict:
      lowerCamelCase_       	=		self.get_image_processor()
      lowerCamelCase_       	=		self.get_tokenizer()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

      lowerCamelCase_       	=		processor.batch_decode(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.batch_decode(__SCREAMING_SNAKE_CASE   )

      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Any   )	->						str:
      lowerCamelCase_       	=		self.get_image_processor()
      lowerCamelCase_       	=		self.get_tokenizer()

      lowerCamelCase_       	=		VisionTextDualEncoderProcessor(tokenizer=__SCREAMING_SNAKE_CASE			,		image_processor=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		self.prepare_image_inputs()

      lowerCamelCase_       	=		processor(text=__SCREAMING_SNAKE_CASE			,		images=__SCREAMING_SNAKE_CASE   )

      self.assertListEqual(list(inputs.keys()   )			,		processor.model_input_names   )












"""simple docstring"""





def 	lowerCamelCase__			(				_lowerCamelCase			:						list[list[int | float]]					) ->							int:
  lowerCamelCase_       	=		len(_lowerCamelCase					)
  lowerCamelCase_       	=		len(matrix[0]					)
  lowerCamelCase_       	=		min(_lowerCamelCase						,							_lowerCamelCase					)

  for row in range(_lowerCamelCase					):
    # Check if diagonal element is not zero
    if matrix[row][row] != 0:
      # Eliminate all the elements below the diagonal
      for col in range(row + 1						,							_lowerCamelCase					):
        lowerCamelCase_       	=		matrix[col][row] / matrix[row][row]
        for i in range(_lowerCamelCase						,							_lowerCamelCase					):
          matrix[col][i] -= multiplier * matrix[row][i]
    else:
      # Find a non-zero diagonal element to swap rows
      lowerCamelCase_       	=		True
      for i in range(row + 1						,							_lowerCamelCase					):
        if matrix[i][row] != 0:
          lowerCamelCase_	,      lowerCamelCase_       	=		matrix[i], matrix[row]
          lowerCamelCase_       	=		False
          break
      if reduce:
        rank -= 1
        for i in range(_lowerCamelCase					):
          lowerCamelCase_       	=		matrix[i][rank]

            # Reduce the row pointer by one to stay on the same row
      row -= 1

  return rank


if __name__ == "__main__":
  import doctest

  doctest.testmod()




